Frost prediction with machine learning techniques

Autores
Verdes, Pablo Fabián; Granitto, Pablo Miguel; Navone, Hugo Daniel; Ceccatto, Hermenegildo Alejandro
Año de publicación
2000
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Frost is the condition that exists when the temperature of the earth's surface and earthbound objects falls below freezing (0°C). These events may have serious consequences on crop production, so actions must be taken to minimize damaging effects. In particular, temperature predictions are of much help in frost protection decisions by providing the hortoculturist with warnings of critical temperatures. Consequently, reliable temperature prediction methods would be of significant economic value. These methods are usually empirical formulae based on significant correlations between the minimum temperature observed towards the end of the night and one or several meteorological variables measured at least several hours before this minimum is reached. These empirical regressions employ antecedent air temperature, various humidity measures, wind speed and cloud cover values. In this work we explore the possibility of developing an empirical prediction system for frost protection of fruits and vegetables in the southern part of the Santa Fe province in Argentina, in the region covered by the agrometeorological station located at Zavalla (33°01′S, 60°53′W). To this end we consider a handful of Machine Learning techniques usually employed in regression and classification problems, including Artificial Neural Networks, Simple Bayes classifiers and k-Nearest Neighbors. The results obtained in this preliminary study reveal a very noisy structure of the data that allows for only a slight improvement in performance of some of these more sophisticated nonlinear techniques over the standard (linear) multivariate regression equations.
I Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
frost prediction
machine learning
regression
classification
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23444

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spelling Frost prediction with machine learning techniquesVerdes, Pablo FabiánGranitto, Pablo MiguelNavone, Hugo DanielCeccatto, Hermenegildo AlejandroCiencias Informáticasfrost predictionmachine learningregressionclassificationFrost is the condition that exists when the temperature of the earth's surface and earthbound objects falls below freezing (0°C). These events may have serious consequences on crop production, so actions must be taken to minimize damaging effects. In particular, temperature predictions are of much help in frost protection decisions by providing the hortoculturist with warnings of critical temperatures. Consequently, reliable temperature prediction methods would be of significant economic value. These methods are usually empirical formulae based on significant correlations between the minimum temperature observed towards the end of the night and one or several meteorological variables measured at least several hours before this minimum is reached. These empirical regressions employ antecedent air temperature, various humidity measures, wind speed and cloud cover values. In this work we explore the possibility of developing an empirical prediction system for frost protection of fruits and vegetables in the southern part of the Santa Fe province in Argentina, in the region covered by the agrometeorological station located at Zavalla (33°01′S, 60°53′W). To this end we consider a handful of Machine Learning techniques usually employed in regression and classification problems, including Artificial Neural Networks, Simple Bayes classifiers and k-Nearest Neighbors. The results obtained in this preliminary study reveal a very noisy structure of the data that allows for only a slight improvement in performance of some of these more sophisticated nonlinear techniques over the standard (linear) multivariate regression equations.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2000-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23444enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:26Zoai:sedici.unlp.edu.ar:10915/23444Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:26.833SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Frost prediction with machine learning techniques
title Frost prediction with machine learning techniques
spellingShingle Frost prediction with machine learning techniques
Verdes, Pablo Fabián
Ciencias Informáticas
frost prediction
machine learning
regression
classification
title_short Frost prediction with machine learning techniques
title_full Frost prediction with machine learning techniques
title_fullStr Frost prediction with machine learning techniques
title_full_unstemmed Frost prediction with machine learning techniques
title_sort Frost prediction with machine learning techniques
dc.creator.none.fl_str_mv Verdes, Pablo Fabián
Granitto, Pablo Miguel
Navone, Hugo Daniel
Ceccatto, Hermenegildo Alejandro
author Verdes, Pablo Fabián
author_facet Verdes, Pablo Fabián
Granitto, Pablo Miguel
Navone, Hugo Daniel
Ceccatto, Hermenegildo Alejandro
author_role author
author2 Granitto, Pablo Miguel
Navone, Hugo Daniel
Ceccatto, Hermenegildo Alejandro
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
frost prediction
machine learning
regression
classification
topic Ciencias Informáticas
frost prediction
machine learning
regression
classification
dc.description.none.fl_txt_mv Frost is the condition that exists when the temperature of the earth's surface and earthbound objects falls below freezing (0°C). These events may have serious consequences on crop production, so actions must be taken to minimize damaging effects. In particular, temperature predictions are of much help in frost protection decisions by providing the hortoculturist with warnings of critical temperatures. Consequently, reliable temperature prediction methods would be of significant economic value. These methods are usually empirical formulae based on significant correlations between the minimum temperature observed towards the end of the night and one or several meteorological variables measured at least several hours before this minimum is reached. These empirical regressions employ antecedent air temperature, various humidity measures, wind speed and cloud cover values. In this work we explore the possibility of developing an empirical prediction system for frost protection of fruits and vegetables in the southern part of the Santa Fe province in Argentina, in the region covered by the agrometeorological station located at Zavalla (33°01′S, 60°53′W). To this end we consider a handful of Machine Learning techniques usually employed in regression and classification problems, including Artificial Neural Networks, Simple Bayes classifiers and k-Nearest Neighbors. The results obtained in this preliminary study reveal a very noisy structure of the data that allows for only a slight improvement in performance of some of these more sophisticated nonlinear techniques over the standard (linear) multivariate regression equations.
I Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description Frost is the condition that exists when the temperature of the earth's surface and earthbound objects falls below freezing (0°C). These events may have serious consequences on crop production, so actions must be taken to minimize damaging effects. In particular, temperature predictions are of much help in frost protection decisions by providing the hortoculturist with warnings of critical temperatures. Consequently, reliable temperature prediction methods would be of significant economic value. These methods are usually empirical formulae based on significant correlations between the minimum temperature observed towards the end of the night and one or several meteorological variables measured at least several hours before this minimum is reached. These empirical regressions employ antecedent air temperature, various humidity measures, wind speed and cloud cover values. In this work we explore the possibility of developing an empirical prediction system for frost protection of fruits and vegetables in the southern part of the Santa Fe province in Argentina, in the region covered by the agrometeorological station located at Zavalla (33°01′S, 60°53′W). To this end we consider a handful of Machine Learning techniques usually employed in regression and classification problems, including Artificial Neural Networks, Simple Bayes classifiers and k-Nearest Neighbors. The results obtained in this preliminary study reveal a very noisy structure of the data that allows for only a slight improvement in performance of some of these more sophisticated nonlinear techniques over the standard (linear) multivariate regression equations.
publishDate 2000
dc.date.none.fl_str_mv 2000-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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